Table Of ContentLihui Wang · Xi Vincent Wang
Cloud-Based
Cyber-Physical
Systems in
Manufacturing
Cloud-Based Cyber-Physical Systems
in Manufacturing
Lihui Wang Xi Vincent Wang
(cid:129)
Cloud-Based Cyber-Physical
Systems in Manufacturing
123
Lihui Wang XiVincent Wang
Department ofProduction Engineering Department ofProduction Engineering
KTH RoyalInstitute of Technology KTH RoyalInstitute of Technology
Stockholm Stockholm
Sweden Sweden
ISBN978-3-319-67692-0 ISBN978-3-319-67693-7 (eBook)
https://doi.org/10.1007/978-3-319-67693-7
LibraryofCongressControlNumber:2017957672
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Preface
Newtechnologiesinmanufacturingaretightlyconnectedtoinnovation.Theyhave
thus been the key factors that support and influence a nation’s economy since the
eighteenth century, from stream engines to Industry 4.0. As the primary driving
force behind economic growth and sustainabledevelopment, manufacturing serves
as thefoundation ofand contribute to other industries, with products ranging from
heavy-duty machinery tohi-tech home electronics. In the past centuries, they have
contributedsignificantlytomoderncivilisationandcreatedthemomentumthatstill
drivestoday’seconomyandsociety.Despitemanyachievements,wearestillfacing
challenges due to growing complexity and uncertainty in manufacturing, such as
adaptability to uncertainty, resource and energy conservation, ageing workforce,
and secure information sharing. Researchers and engineers across organisations
often find themselves in situations that demand advanced new technologies when
dealing with new challenges in daily activities related to manufacturing, which
cannot be addressed by existing approaches.
Targeting the challenges in solving daily problems, over the past a few years,
researchers have focused their efforts on innovative approaches to improving the
adaptability to complex situations on shop floors and energy efficiency along the
life cycle of products. New technologies and innovations include cyber-physical
system (CPS), cloud manufacturing (CM), Internet of Things (IoT), big data ana-
lytics, which are related to embedded systems and system of systems. These new
technologies are now driving industry towards yet another revolution and are
referredtotheGermaninitiativeIndustry4.0.Whiletheseeffortshaveresultedina
large volume of publications recently and impacted both present and future prac-
tices in factories and beyond, there still exists a gap in the literature for a focused
collectionofknowledgededicatedtocloud-basedCPSinmanufacturing.Tobridge
thisgapandpresentthestateofthearttoamuchbroaderreadership,fromacademic
researchers to practicing engineers, is the primary motivation behind this book.
The first three chapters form Part 1 of this book on the literature surveys and
trends. Chapter 1 begins with a clear definition of cloud computing (CC) versus
cloud manufacturing (CM). CC and CM represent the latest advancement and
applications of the cloud technologies in computing and manufacturing,
v
vi Preface
respectively.TheaimofChap.1istoprovideacomprehensiveintroductiontoboth
CC and CM and to present their status and advancement. The discussions on CC
and CM are extended in Chap. 2 to cover the latest advancement of CPS and IoT,
especiallyinmanufacturingsystems.TocomprehensivelyunderstandCPSandIoT,
a brief introduction to both of them is given, and the key enabling technologies
related to CPS and IoT are outlined. Key features, characteristics, and advance-
ments are explained, and a few applications are reported to highlight the latest
advancementinCPSandIoT.Chapter3thenprovidesanoverviewofcybersecurity
measures being considered to ensure the protection of data being sent to physical
machines in a cybernetic system. While common to other cybernetic systems,
security issues in CM are focused in this chapter for brevity.
Part 2ofthis bookfocusesoncloud-based monitoring,planning,andcontrolin
CPSandisconstitutedfromfourchapters.Targetingdistributedmanufacturing,the
scope of Chap. 4 is to present an Internet- and web-based service-oriented system
formachineavailabilitymonitoringandprocessplanning.Particularly,thischapter
introduces a tiered system architecture and introduces IEC 61499 function blocks
for prototype implementation. It enables real-time monitoring of machine avail-
ability and execution status during metal-cutting operations, both locally or
remotely. The closed-loop information flow makes process planning and moni-
toring two feasible services for the distributed manufacturing. Based on the
machine availability and the execution status, Chap. 5 introduces Cloud-DPP for
collaborative and adaptive process planning in cloud environment. Cloud-DPP
supports parts machining with a combination of milling and turning features and
offers process planning services for multi-tasking machining centres with special
functionalities to minimise thetotal numberof set ups.InChap.6, theCloud-DPP
is linked to physical machines by function blocks to form a CPS. Within the CPS,
functionblocks runatcontrollevelwithembeddedmachining informationsuchas
machiningsequenceandmachiningparameterstofacilitateadaptivemachining.To
utilise the machines properly, right maintenance strategies are required. Chapter 7
reviews the historical development of prognosis theories and techniques and pro-
jects their future growth in maintenance enabled by the cloud infrastructure.
Techniques for cloud computing are highlighted, as well as their influence on
cloud-enabled prognosis for manufacturing.
Sustainablerobotic assembly inCPSsettingsiscovered inChaps.8through11
and organised into Part 3 of this book. Chapter 8 explains how to minimise a
robot’s energy consumption during assembly. Given a trajectory and based on the
inversekinematicsanddynamicsoftherobot,asetofattainableconfigurationsfor
therobotcanbedetermined,perusedbycalculatingthesuitableforcesandtorques
on thejointsand links of therobot. The energy consumptionis then calculated for
each configuration and based on the assigned trajectory. The ones with the lowest
energy consumption are chosen for robot motion control. This approach becomes
instrumental and can be wrapped as a cloud service for energy-efficient robotic
assembly. Another robotic application is for human–robot collaborative assembly.
Chapter 9 addresses safety issues in human–robot collaboration. This chapter first
Preface vii
reviewsthetraditionalsafetysystemsandthenpresentsthelatestaccomplishments
in active collision avoidance through immersive human–robot collaboration by
using two depth cameras installed carefully in a robotic assembly cell. A remote
roboticassemblysystemisthenintroducedinthesecondhalfofthechapterasone
cloud robotic application. In Chap. 10, recent cloud robotics approaches are
reviewed.Functionblock-basedintegrationmechanismsareintroducedtointegrate
varioustypesofmanufacturingfacilitiesincludingrobots.Bycombiningcloudwith
robots in form of cloud robotics, it contributes to a ubiquitous and integrated
cloud-based CPS system in robotic assembly. Chapter 11 further explores the
potential of establishing context awareness between a human worker and an
industrial robot for physical human–robot collaborative assembly. The context
awareness between the human worker and the industrial robot is established by
applying gesture recognition, human motion recognition, and augmented reality
(AR)-based worker instruction technologies. Such a system works in a
cyber-physical environment, and its results are demonstrated through case studies.
InPart4ofthisbook,theaspectofCPSsystemsdesignandlifecycleanalysisis
sharedbyChaps.12–15.Chapter12presentsthearchitecturedesignofcloudCPSin
manufacturing. Manufacturing resources and capabilities are discussed in terms of
cloud services. Aservice-oriented,interoperable CMsystemisintroduced. Service
methodologies are developed to support two types of cloud users, customer user
versusenterpriseuser,alongwithstandardiseddatamodelsdescribingcloudservice
andrelevantfeatures.Twocasestudiesarerevealedtoevaluatethesystem.System
design is extended in Chap. 13 to cover lifecycle analysis and management of
products. In this chapter, CM is extended to the recovery and recycling of waste
electricalandelectronicequipment(WEEE).Cloudservicesareusedintherecovery
and recycling processes for WEEE tracking and management. These services
includeallthestakeholdersfromthebeginningtotheendoflifeoftheelectricaland
electronicequipment.Aproducttrackingmechanismisalsointroducedwiththehelp
ofthequickresponse(QR)codemethod.Chapter14focusesonbigdataanalytics.
In order to minimise machining errors in advance, a big data analytics-based fault
prediction approach is presented for shop-floor job scheduling, where machining
jobs, machining resources, and machining processes are represented by data attri-
butes.Basedontheavailabledataontheshopfloor,thepotentialfault/errorpatterns,
referringtomachiningerrors,machinefaults,maintenancestates,etc.,areminedto
discover unsuitable scheduling arrangements before machining as well as the pre-
dictionofupcomingerrorsduringmachining.Chapter15presentsasummaryofthe
current status and the latest advancement of CM, CPS, IoT, and big data in manu-
facturing. Cloud-based CPS shows great promise in factories of the future in the
areasoffuturetrendsasidentifiedattheendofthischapter.Italsooffersanoutlook
of research challenges and directions inthe subject areas.
All together, the fifteen chapters provide an overview of some recent R&D
achievements of cloud-based CPS applied to manufacturing, especially machining
andassembly.Webelievethatthisresearchfieldwillcontinuetobeactiveforyears
to come.
viii Preface
Finally, the authors would like to express their appreciations to Springer for
supporting this book project and would especially like to thank Anthony Doyle,
SeniorEditorforEngineering,andAmudhaVijayarangan,ProjectCoordinator,for
their patience, constructive assistance, and earnest cooperation, both with the
publishing venture in general and with the editorial details. We hope that readers
find this book informative and useful.
Stockholm, Sweden Lihui Wang
September 2017 Xi Vincent Wang
Acknowledgements
It took a long time for the authors to prepare the book materials. Several people
contributedtothisbookdirectlyorindirectlyduringtheprocess.Theauthorswould
like to take this opportunity to express their sincere appreciations to Dr. Abdullah
Alhusin Alkhdur, Dr. Wei Ji, Dr. Yongkui Liu, Mr. Mohammad Givehchi, Mr.
Hongyi Liu, and Mr. Sichao Liu for their fine contributions to this book. Their
commitment, enthusiasm, and assistance are what made this book possible.
TheauthorsarealsogratefultoProfs.RobertGao,MauroOnori,IhabRagai,and
Martin Törngren for their cooperative work and technical expertise, the results of
whichaddedagreatvaluetothisbooktocompletethecoverageofnewknowledge.
Finally, the authors are deeply thankful to their families with ultimate respect
and gratitude for their continuous support during the process of this book prepa-
rationineveningsandweekends.Withouttheirunderstandingandencouragement,
this book would never be materialised.
ix
Contents
Part I Literature Survey and Trends
1 Latest Advancement in Cloud Technologies . . . . . . . . . . . . . . . . . . 3
1.1 Introduction to Cloud Computing . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1 Historical Evolution and Background . . . . . . . . . . . . . 4
1.1.2 Concept. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.3 Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.1.4 Cloud Platforms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.1.5 Tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.1.6 Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.2 Cloud Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.2.1 Historical Evolution and Background . . . . . . . . . . . . . 18
1.2.2 Concept. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.2.3 Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.2.4 Research Initiatives. . . . . . . . . . . . . . . . . . . . . . . . . . . 27
1.2.5 Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
1.2.6 Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
1.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2 Latest Advancement in CPS and IoT Applications . . . . . . . . . . . . . 33
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.2 Key Enabling Technologies in CPS and IoT. . . . . . . . . . . . . . . 35
2.2.1 Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . 36
2.2.2 Could Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.2.3 Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.2.4 Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.2.5 RFID Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.3 Key Features and Characteristics of CPS and IoT. . . . . . . . . . . 40
xi
